The Neural Basis of Empathy and Empathic Behavior in the Context of Chronic Trauma
Bibliographic record
Abstract
Background: Accumulating evidence in social neuroscience suggests that mature human empathy relies on the integration of two types of processes: a lower-order process mainly tapping into automatic and sensory mechanisms and a higher-order process involving affect and cognition and modulated by top-down control. Studies have also indicated that neural measures of empathy often correlate with behavioral measures of empathy. Yet, little is known on the effects of chronic trauma on the neural and behavioral indices of empathy and the associations among them. Methods: Mothers exposed to chronic war-related trauma and non-exposed controls (N=88, N=41 war-exposed) underwent magnetoencephalography (MEG) while watching stimuli depicting vicarious emotional distress. Maternal empathic behavior was assessed during mother-child interaction involving a joint task. Results: Empathy-evoking vignettes elicited response in alpha rhythms in a network involving both sensorimotor and viceromotor (anterior insula) regions, suggesting integration of the sensory and affective components of empathy. Whereas exposure to chronic stress did not affect the level of neural activations in this network, it reduced maternal empathic behavior. Furthermore, trauma exposure impaired the coherence of brain and behavior; only among controls, but not among trauma-exposed mothers, the neural basis of empathy was predicted by maternal empathic behavior. Conclusions: Chronic stress takes a toll on the mother's empathic ability and indirectly impacts the neural basis of empathy by disrupting the coherence of brain and behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".